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By Jaroslav Briancin, Katarína Gáborová, María Florencia Márquez-Zavalía, Marcela Achimovičová, Ol’ga Šestinová, Pavel Škácha

Naumannite (Ag,Se), a silver selenide mineral, was first discovered in 1828. This study investigates the structure, morphology, chemical composition, and leaching kinetics of naumannite. Mechanical activation in a planetary ball mill increased its specific surface area, enhancing the leaching rate of silver in nitric acid (HNO3). The study found that mechanical activation significantly improves silver recovery.

Jul 1, 2023

By Ajit Godbole, Ping Chang, Ramakrishna Morla, Purushotham Tukkaraja, Shivakumar Karekal

This paper presents the results of a study on the optimum auxiliary fan location to control air recirculation in dead-end workings where diesel-powered vehicles operate. Investigations were conducted with various secondary fan locations from the dead-end crosscut and with the intake air quantities varied using different auxiliary fans to control air recirculation and diesel particulate matter (DPM).

Jul 1, 2023

By Leping Liu, Li‑Ting Chen, Sachin Urade, Pengbo Chu

Mining industry requires constant adaptation to its practices in response to the dynamic change of the societal demand. Little information exists regarding how the US metal mining engineering education is prepared to meet with its industry needs due to the increase in critical minerals driven by the green energy transitions. This study aims to provide industrial perspectives on the current US metal mining engineering education and areas for improvement. An online survey with both closed- and open-ended questions were designed to seek opinions from the US mining industry. Findings indicated a shortage of qualified graduates from the current US metal mining engineering education system. The qualifications need to be improved include engineering sciences underlying mining methods, mining design experience, mining feasibility study, the connection between theory and practice, and understanding of the overall mining operation. The survey also identified future desired qualifications. The most desired ones in the next 5 years include an ability to acquire and apply new knowledge as needed and sufficient experience in the field. Respondents, regardless of the nature of their affiliated mining companies, unanimously recommended that collaborations between the industry and academia should be enhanced. The analysis of the results concluded four recommendations: (1) involve more multiple stakeholders in reforming mining education programs, (2) reinforce field experiences as a key part of mining engineering education programs, (3) enhance a closer collaboration between academia and industry, and (4) integrate emerging technologies (e.g., artificial intelligence/virtual reality) guided by pedagogical theories into new mining engineering curriculums.

Jun 26, 2023

By Juan L. Yarmuch, Hyam Rubinstein

Open-pit mine planning is a key process in the definition of mineral reserves and their efficient removal. Ore in open-pit mines is extracted bymining sequential pits called pushbacks. Pushbacks need to satisfy complex geometrical constraints to ensure the efficient usage of equipment. To date, no algorithms that both account for those constraints and maximizeNPV, are commercially available. Therefore, pushback design is done by engineers relying on their expertise to incorporate practical features intothe output of commercial tools. In this article, we present a new algorithm that produces close to optimal practical pushbacks and we illustrate its performance in real mines.

Jun 25, 2023

By Chris Roos

The mining industry has made strides in planning for operations to be safe for the workforce and public, to protect the natural environment, and to maximize value for shareholders. However, many of the techniques used to accomplish these goals are based on estimated models of mineral content, production efficiency, and economic performance. The industry has begun to recognize the need for understanding the uncertainty in these models, how it affects their decisions, and what the potential impacts are for each project. The challenge has become how technical professionals can effectively communicate the impact of this uncertainty and its importance to both technical and non-technical audiences. This paper presents mining engineers (and other technical professionals) with traditional and modern principles of data visualization and technical communication that can assist with communicating the effects of uncertainty in the mine planning process.

Jun 25, 2023

By A. Adhikari, P. Tukkaraja, S. Jayaraman Sridharan

Shafts are critical components of the mine ventilation systems and contribute significantly to the mine ventilation pressure. Estimation of shaft pressure losses is an important aspect of mine ventilation planning and shaft frictional pressure losses are extensively studies. Shock losses contributed by shaft buntons are estimated using the interference factor model developed half a century ago. This study aims to develop a better performing prediction model for shaft buntons using state of the art experiment and numerical data, and machine learning techniques. Results show that the present prediction model is performing poorly, and the prediction performance can be improved by incorporating additional predictor variables. The Artificial Neural Network standardized Machine Learning model developed in this study is able to predict the shaft bunton shock loss with high accuracy (R2 = 0.956).

Jun 25, 2023

By Alireza Kamrani, Yashar Pourrahimian, Hooman Askari-Nasab

In-pit crushing and conveying (IPCC) is an option to decrease the enormous operating costs that a truck shovel can introduce in an open-pit mine. Finding the best locations for the IPCC over the mine life will impose a new set of requirements for the mine planning. Through a two-step mathematical programming model alongside the road network, this method finds the optimal in-pit crusher locations and relocation times first and then optimizes the long-term mine schedule. The model is verified by a real mine. The results show that the truck travel time could be reduced by 500% and the discounted cash flow increased by 8% with an optimized schedule considering the crusher placement.

Jun 25, 2023

By Javad Sattarvand, Amin Moniri-Morad

Predicting equipment failure behavior plays a critical role in providing a safe operation. Although various reliability evaluation methods are available for estimating failure behavior profiles, these approaches are based on mathematical formulation, leading to actual operation simplification and improper estimation of failure trends. This study proposes a discrete-event simulation (DES) approach to model fleet failure behavior. This approach can model the maintenance strategies in multi-component systems with minimum simplifications. In this regard, the first step was to collect a historical failure time dataset (e.g., the time between failures). Then, the DES model is established to predict the severity of failure events and design maintenance strategy considering actual operation circumstances. Afterward, the maintainability analysis was performed to estimate equipment downtime and availability. The achieved results showed that the system availability was about 85%. Also, the findings demonstrated that the DES could efficiently predict the equipment failure behavior using various stochastic events.

Jun 25, 2023

By Robert Bissonette, Samir Sbai

A critical component of implementing autonomous systems is the ability to use information from a robust sensory and information system to make sound, predictable, and repeatable decisions to assure safety. This means that any such system would need to evaluate what objects (people, equipment, boulders, etc.) of interest exist in that environment (with relevant data about identity, trajectory, characteristics, etc.), project a future continuum of those objects (including considerations for variabilities), and make control intervention choices to minimize risk due to exposures in the present operational area. The components of an overall system would include: a) sensor and data fusion, b) probability projection of future states, c) generation and evaluation of alternate futures, and d) implementation of best course of action to reduce risk. The National Institute for Occupational Safety and Health (NIOSH) Spokane Mining Research Division (SMRD) is studying this framework for applications for autonomous mining equipment. This paper focuses on using information from the sensor fusion engine to generate a continuous projection of future and alternate states (b and c above) incorporating the factors of confidence, accuracy, tolerance, and other variabilities. We refer to this as Machine Situational Awareness (MSA).

Jun 25, 2023

By Jan Luca Grunow, Ansgar Bendiek, Klaus Böde

According to the Markowitz portfolio theory the value of a diversified portfolio is higher than the sum of the parts of a portfolio, which is an important consideration for the correct valuation. This is valid for real estate portfolios as well as for mining assets (or enterprises). This paper analyses the application of the portfolio theory to a diversified portfolio of assets in the real estate industry and draws conclusions on further research work to be done in the minerals industry. The volume of real estate transactions is increasing over recent years. As such, the nature of those transactions is constantly changing and in some cases portfolios become more heterogeneous as they are composed of different property categories, e.g. locations and types of use. This makes their economic evaluation more difficult. This paper focuses on the acquisition of a fairly heterogeneous real estate property portfolio purchased by “Commerz Real” in Germany in Q4 2019. For economic evaluation the widely used Markowitz portfolio theory (Markowitz, 1952) was applied. It was investigated whether diversification effects could achieve added value in the form of a reduction in risk compared to the sum of their individual components. As a result the findings of this research could be applied to other heterogeneous real estate transactions and even to mine property portfolio or PPP1 project portfolio transactions.

Jun 25, 2023

By Enrique Jelvez, Nelson Morales Varela, Christian Ihle, Joaquin Silva

All mineral beneficiation processes require water for its execution. Therefore, the availability and adequate management of water are key to the existence of any mine operation. However, many mines’ operations are affected by water scarcity and risk an increase of operational costs or even production in some cases. This paper proposes a model to minimize the losses of water due to evaporation and re-absorption by adjusting the active and inactive periods of the discharge in different points of the tailing. When applied, the model determines optimal or nearly optimal discharge operation in all the scenarios that were evaluated.

Jun 25, 2023

By J. Sattarvand, P. C. Augustine

Despite significant safety improvements, the mining industry remains one of the most hazardous occupations globally and the evacuation of Miners, when an uncontrollable incident occurs in the mine, is the best bet to saving lives. However, Human factors/behaviors during an emergency are likely to influence the evacuation performance. Other industries have made a significant effort to determine these human factors that can impede evacuation performance, however, the current state-of-the-art in mine evacuation or self-rescue is that the evacuation route is predetermined, and a static sign is used to direct miners to the predetermined safe location. This method is limited in representing actual conditions that arise in a real emergency, neglecting the different behaviors displayed by humans. As optimization of the evacuation of a mine plays a fundamental role in emergencies and modeling evacuation behavior and movement of miners is a complex task, this research utilized Agent-Based simulations to simulate the evacuation behavior and performance. It was observed that people with the smart evacuation device made fewer mistakes and arrived at the safe place in less time compared to those have no access to the device. The Mathematical model used in optimizing the shortest path to a safe place is also explained in this paper.

Jun 25, 2023

By Javad Sattarvand, Frimpong Kwaku Asare

The safe evacuation of miners during an emergency in the shortest amount of time is one of the most essential features of a mining operation. Despite advancements in mine evacuation research, the usage of mine vehicles during evacuation has not been considered. The majority of recent studies have been done on miners’ evacuation on foot. Mathematical formulations including Minimum Cost Network Flow (MCNF) models, Dijkstra's algorithm, Floyd-Warshall algorithm, and Ant Colony Optimization models have been used to solve mine evacuation problems. These models, which concentrate on determining the shortest escape routes during the evacuation, have been found to be computationally expensive and may not offer the best solutions in some cases. The goal of this paper is to incorporate light vehicles in the evacuation process using a mixed-integer programming approach. The methodology aims to minimize the total time required to evacuate the mine, ensuring minimal exposure of the miners to the adverse conditions that triggered the evacuation.

Jun 25, 2023

By Joseph Githiria, Khadija Omar Said

Mineable material is defined using cut-off grade either as geological or economic cut-off grade. Both geological and economic cut-off grade affects the output generated in an open-pit mining operation. It distinguishes the amount of ore sent to the mill for processing and waste to the dump. This consequently affects the overall profits generated from the operation hence the need for cut-off grade optimization. There are several mining parameters that are considered in a typical cut-off grade model. They include economic, geological, and operational parameters. They have a significant effect to influence the cut-off grade selected in mining operations at any given period. Some of the parameters are stochastic in nature by having a random probability distribution that may not be predicted precisely. When selecting cut-off grades in a mine operation, the mining engineers must consider the dynamic nature of the operational parameters in order to maximize the net present value of the project. Modifying Lane’s cut-off grade model which was developed in 1964 to incorporate the stochastic parameters in the approach helps improve the net present value as shown in the paper. The modified Lane’s approach is developed into a stochastic computer-aided application code-named NPVMining. Using NPVMining, selected stochastic parameters are considered in this exercise to show the effect on the output (net present value). The selected parameters (price and grade-tonnage) are varied by ± 10%. NPVMining has a graphical user interface that makes it easy to use and displays results in a user-friendly way. It gives the user the choice to manipulate the data depending on the study being undertaken or any other factor affecting the specific mine. This paper analyses the effect of each of the selected mining parameters on cut-off grade using NPVMining to show the significance of monitoring their variance.

Jun 25, 2023

By Ehsan Moosavi, Kamyar Tolouei

In open-pit mines, the long-term production scheduling optimization problem (LTPSOP) is a complicated problem that contains constraints, large datasets and uncertainties. Due to its dimensions and NP-hard nature, it is usually difficult to find an ideal solution to the LTPSOP. The optimal schedule generally restricts the ore, metal and waste tonnages, average grades, and cash flows of each period. In this paper, to synthesize grade uncertainty into the strategic schedule, a stochastic integer programming (SIP) framework is presented to LTPSOP. The objective function of the SIP model is to maximize the net present value and minimize the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. Instead of applying one estimated orebody model as input to optimize the production schedule, a set of equally probable orebody realizations is applied to synthesize grade uncertainty in the strategic schedule and produce a more profitable and riskbased production schedule. To solve the LTPSOP, the hybrid approach with the augmented Lagrangian relaxation method, the Harris Hawks optimization, the Random Forest method and Monte Carlo method was proposed. To indicate the applicability of the model, a case study on a gold mine was implemented. The framework displays the capability to minimize risk and improvement in the expected net present value and financial profitability .

Jun 25, 2023

By Yashar Pourrahimian, Roberto Noriega

Truck fleet dispatching plays a crucial role in reducing operational costs and fulfilling operational targets in open-pit mining operations and is subject to large uncertainty arising from the operating cycles of trucks and loading equipment. This paper proposes a reinforcement learning-based truck dispatching system. Reinforcement learning is a machine learning area that deals with learning an optimal sequential decision-making strategy in an uncertain environment. An open-pit load-and-haul simulation is developed that also captures the truck movements and interactions in the shared road network, and a Neural Network is successfully trained to suggest truck dispatching decisions in a real-time manner.

Jun 25, 2023

By W Medina, G Angulo, A Anani

A current challenge faced by the mining industry is solving the integrated transition mine problem (TMP) that maximizes the net present value (NPV). This study aims to develop an optimization framework capable of solving the TMP. The problem is NP-hard and therefore computationally intractable. Three ad hoc approaches – Exact solution, Benders’ decomposition, and Benders’ decomposition with Bienstock-Zuckerberg (BZ) algorithm – are compared for different scheduling periods. The TMP is formulated as a mixed integer linear programming model, implemented in Python, and solved with the Gurobi optimizer. The results showed that Bender's decomposition with the BZ algorithm outperforms the other approaches in execution time, computational cost, and feasibility to address large problems in exchange for a marginal cost in the quality of the NPV obtained. In addition, the probability of finding the optimal point of transition is higher regardless of the number of feasible crown pillar locations evaluated. This is achieved through the optimality cuts proposed in the algorithm.

Jun 25, 2023

By Bartłomiej Bursa, Paweł Stefanek, Paweł Stefaniak, Sergii Anufriiev, Wioletta Koperska, Maria Stachowiak

The article presents the main functionality of the DSS class system for monitoring and stability assessment of tailings storage facilities. The presented demonstrator was deployed on the example of the Żelazny Most reservoir - the largest active facility of this type in Europe and the second one in the world. Considered a high-risk structure, it demands a system capable of online analysis of big data from various sources using machine learning, 3D modeling, or GIS mapping with the functions of interactive time-spatial analysis. In this paper, we present a complex calculation module with an integrated user interface.

Jun 25, 2023

By Kate Willa Brown Requist, Eric Lutz, Moe Momayez

As air quality sensor networks become increasingly popular in underground coal mines, it is important to generate paradigms for the application of the collected data. To date, air quality sensing has been primarily used as an early warning system for hazardous air conditions. Using data collected from a network of sensors in a US underground coal mine, we have created multiple visualization methods to show the interactions of carbon monoxide evolution with ventilation airflow. These visualization methods can allow for further analysis of the source of contaminant, as well as better data resolution across the area of concern within the mine. By utilizing univariate spatial interpolations, we present methods for identifying the movement of carbon monoxide at one-minute intervals. The resulting visualizations display the evolution of carbon monoxide concentration across a sizeable study area over a period of 14 minutes.

Jun 25, 2023

By Mario E. Rossi

Often geometallurgical models are prepared at various project stages from very few data and are assumed to be representative of very large tonnages. These may include recovery models; crushing and grinding parameters and models; models of concentrate grades; models of acid consumption; etc. These models commonly do not carry a measure of uncertainty or variability, and are taken at face value at the time of design and planning. This paper proposes a proactive approach to modelling variability, providing uncertainty estimates and thus allowing for risk analysis and mitigation of geometallurgical models. The variability model is built up by understanding the limitations of the pillars of a geometallurgical model: data uncertainty; geologic model uncertainty; domain definition uncertainty; and parameter estimation uncertainty. Of these factors, typically domain definition is the most significant contributor to geometallurgical model variability. First, the fundamental supports for these models are briefly discussed, with domain definition emphasized. Second, two methods for uncertainty modelling are proposed. The first, more applicable to conceptual or early-stage projects, is a simpler and more expedient alternative that provides initial assessments of variability. This is intended to aid management in decision-making related to budgeting, including further sampling and testing. The second, a block-by-block approach that allows a detailed assessment of risk for specific designs and mine plans, thus providing further opportunities for mitigation. In the example provided, emphasis is placed in communicating this variability to both management and technical personnel in the form of “measured-indicated-inferred” indicators and also block-by-block confidence intervals.

Jun 25, 2023